Splunk SIEM: Key Features, Limitations and Alternatives

What Is Splunk SIEM (Enterprise Security)? 

Splunk SIEM, also known as Splunk Enterprise Security (ES), is a security information and event management system offering security monitoring and analysis. It attempts to allow organizations to gain visibility into their IT infrastructure and detect potential security threats. 

Splunk ES collects and correlates data across multiple sources, providing somewhat real-time insights into security events. This software identifyies anomalies and responds to them over a period of time, mitigating potential damage.

Unlike traditional security systems, Splunk SIEM offers analytics capabilities and machine learning for threat detection. It simplifies incident response through customizable dashboards and alerts. 

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Key Features of Splunk SIEM 

Splunk Enterprise Security (ES) offers a set of features to improve threat detection, investigation, and response:

• SOC workflows: Integrates SIEM and SOAR workflows, offering an interface for detecting, investigating, and responding to threats. This approach intends to reduce mean time to detect (MTTD) and mean time to respond (MTTR).
• Detection versioning: Attempts to automatically manage and track versions of detection content, to enable updates, rollbacks, and backups. The hope is better detection hygiene and management of security configurations.
• Risk-based alerting (RBA): Prioritizes alerts by attributing risk scores to users and systems, in the hopes of reducing false positives and improving SOC productivity.
• Threat topology: Maps the scope of incidents, linking risk and threat objects for investigation and response.
• Behavioral analytics: Utilizes machine learning to analyze user behavior, detect anomalies, and improve threat detection accuracy.
• Investigation workbench: Contains data, intelligence, and context for incident analysis. It includes timelines and ad-hoc search for investigations.
• Adaptive response actions: Provides automated and manual actions for notable events for remediation and incident handling.
• Threat intelligence integration: Used to augment alerts with internal and external threat intelligence sources when available, which is operationalized through Splunk SOAR.
• MITRE ATT&CK framework support: Enables analysts to correlate incidents with the MITRE ATT&CK Matrix for situational awareness and responses when applicable or available.
• Pre-packaged content updates: Includes updated analytic stories and use cases from the Splunk Threat Research Team.

Related Splunk Security Products 

Splunk SOAR (As of Q1 2025)

Splunk SOAR (Security Orchestration, Automation, and Response) assists security operations centers (SOCs) by automating tasks and orchestrating workflows across various tools. It integrates with various third-party tools, supporting automated actions to simplify incident response. 

With its Visual Playbook Editor, users can create custom workflows, in the hopes of improving scalability and usability. It provides case management, threat intelligence integration, and different deployment options (on-premises, cloud-based, or hybrid). Splunk SOAR also integrates with the MITRE ATT&CK and D3FEND frameworks, offering prebuilt playbooks to automate end-to-end use cases.

Splunk Attack Analyzer

Splunk Attack Analyzer attempts to automate the analysis of active threats, such as malware and credential phishing attempts, to provide insights for response. It’s aim is to execute potential attack chains in a sandbox environment, including accessing links and extracting attachments, giving security teams a forensic view of the threat. 

By integrating with Splunk SOAR, the goal is to enable end-to-end automation of threat detection and response workflows. Analysts are intended to interact with malicious content in secure environments and visualize attack chains, for threat investigations, hunting, and operationalize threat intelligence. The tool also offers an API for integrating threat data into other platforms.

Splunk Asset and Risk Intelligence

Splunk Asset and Risk Intelligence intends to deliver asset discovery and risk monitoring from network, endpoint, cloud, and scanning tools. This goal is to provide a unified, updated inventory of assets and identities, for visibility across IT and security operations (SecOps). 

Security teams can conduct investigations with context on assets and identities while identifying compliance gaps using out-of-the-box dashboards. The tool integrates with Splunk ES for asset data in event investigations and connects with tools like ServiceNow CMDB, in attempt to manage unmanaged devices and improve compliance status.

Splunk User Behavior Analytics

Splunk User Behavior Analytics (UBA) focuses on detecting insider threats and advanced attacks based on user and entity behavior. It looks for patterns across data sources such as login activities, file access, and network traffic. 

UBA reduces false positives by correlating events and prioritizing risks based on the context of the behaviors observed such as account takeovers and privilege abuse. By integrating with Splunk ES, UBA intends to help security teams focus on high-priority threats, in the hopes of improving the speed and accuracy of their responses. 

Splunk SIEM Pricing Models 

Splunk Enterprise Security (ES) offers two pricing models: Workload pricing and ingest pricing. Each model is designed to address an organization’s needs and data usage patterns.

Workload pricing is based on the compute and storage resources required to process data within Splunk. This may be ideal for organizations to want to bring in large amounts of data for future use without worrying about precise ingest volume predictions.

Ingest pricing follows a traditional volume-based approach, charging based on the amount of data ingested into Splunk per day. This model may suit organizations with predictable data strategies and clear use cases.

Choosing the right model:

• Workload pricing is suitable for organizations handling diverse or unpredictable data volumes, offering flexibility and control over compute resources.
• Ingest pricing works best for companies with a clear data strategy and predictable data ingestion needs, ensuring cost-efficiency for well-defined use cases.

Limitations of Splunk SIEM 

Splunk Enterprise Security has some limitations that organizations should consider, which may impact its usability, implementation, and cost-effectiveness, particularly for smaller organizations. These limitations were reported by users on the G2 platform:

• High cost: Splunk ES is expensive compared to other SIEM solutions, making it less accessible for smaller organizations or those with tight budgets.
• Implementation complexity: Deploying Splunk ES can be challenging, requiring significant effort, expertise, and time to set up and configure properly.
• Steep learning curve: New users often find the platform’s security features difficult to learn, necessitating extensive training.
• Performance issues under high data volume: When dealing with large volumes of data, the interface may become slow, and search performance can degrade unless optimal queries are used.
• Limited built-in features: Some users report a lack of out-of-the-box capabilities, requiring additional tools or customization to meet specific needs.
• Dependence on customer support: While customer support is responsive, the need for frequent assistance due to the platform’s complexity can be a drawback for less experienced teams.
• Inadequate automated data quality validation: The platform does not include features for automated data quality checks, which may reduce confidence in the reliability of insights.

Notable Splunk SIEM Alternatives and Competitors 

1. Exabeam

Exabeam is a leading provider of security information and event management (SIEM) solutions, combining UEBA, SIEM, SOAR, and TDIR to accelerate security operations. Its Security Operations platforms enables security teams to quickly detect, investigate, and respond to threats while enhancing operational efficiency.

Key Features:

• Scalable log collection and management: The open platform accelerates log onboarding by 70%, eliminating the need for advanced engineering skills while ensuring seamless log aggregation across hybrid environments.
• Behavioral analytics: Uses advanced analytics to baseline normal vs. abnormal behavior, detecting insider threats, lateral movement, and advanced attacks missed by signature-based systems. Customers report that Exabeam helps detect and respond to 90% of attacks before other vendors can catch them.
• Automated threat response: Simplifies security operations by automating incident timelines, reducing manual effort by 30%, and accelerating investigation times by 80%.
• Contextual incident investigation: Since Exabeam automates timeline creation and reduces time spent on menial tasks, it cuts the time to detect and respond to threats by over 50%. Pre-built correlation rules, anomaly detection models, and vendor integrations reduce alerts by 60%, minimizing false positives.
• SaaS and cloud-native options: Flexible deployment options provide scalability for cloud-first and hybrid environments, ensuring rapid time to value for customers. For organizations who can’t, or won’t move their SIEM to the cloud, Exabeam provides a market-leading, full featured, and self-hosted SIEM.
• Network visibility with NetMon: Delivers deep insight beyond firewalls and IDS/IPS, detecting threats like data theft and botnet activity while making investigation easier with flexible searching. Deep Packet Analytics (DPA) also builds on the NetMon Deep Packet Inspection (DPI) engine to interpret key indicators of compromise (IOCs).

Exabeam customers consistently highlight how its real-time visibility, automation, and productivity tools powered by AI, uplevel security talent, transforming overwhelmed analysts into proactive defenders while reducing costs and maintaining industry-leading support. 

2. IBM Security QRadar SIEM

IBM Security QRadar SIEM that might improve the efficiency of SOCs through AI, automation, and with its threat intelligence. It attempts to help SOC analysts reduce noise, prioritize threats, and correlate incidents in a unified interface, hoping to enabling faster detection and response to cyber threats.

Key features include:

• Risk-based alert prioritization: Applies multi-layered risk scoring on observables, hoping that analysts focus only on critical cases.
• User behavior analytics (UBA): Identifies unusual activities and potential insider threats through user and entity behavior patterns.
• Network threat analytics: Monitors and analyzes network activity to detect anomalies and threats.
• Sigma rules integration: Provides support for Sigma, an open-source standard for SIEM detection rules, enabling threat detection.
• Federated search: Allows analysts to query across multiple data sources and platforms for investigations.

3. FortiSIEM

FortiSIEM is a SIEM platform that assists security operations teams by combining centralized event collection, detection analytics, and incident management. It also offers IT and OT (operational technology) support, a built-in configuration management database (CMDB), and generative AI (FortiAI) for investigations and responses. 

Key features include:

• Built-in IT/OT CMDB: Provides automated asset discovery and continuous health and performance monitoring, improving visibility into IT and OT infrastructure.
• Real-time security analytics: Detects threats using correlation rules, UEBA (user and entity behavior analytics), and customizable machine learning models.
• FortiAI generative AI: Embeds generative AI to assist with incident investigation, threat hunting, and reporting, offering natural language queries and actionable insights.
• OSquery endpoint visibility: Supports endpoint forensic monitoring and investigation through integration with OSquery.
• Broad integrations: Includes support for third-party solutions, with integrations with Fortinet products being the base.

4. Microsoft Sentinel

Microsoft Sentinel is a cloud-only SIEM solution that integrates security orchestration, automation, and response (SOAR) capabilities to deliver threat detection, investigation, and response. Built on Microsoft Azure, it can provide organizations with insights, threat hunting hunting tools, and integration options to manage security across environments. 

Key features include:

• Data collection: Collects data across users, devices, applications, and infrastructures, supporting on-premises, cloud, and hybrid environments. Includes connectors for Microsoft and non-Microsoft solutions and supports data ingestion through REST APIs and Syslog.
• Threat detection: Utilizes analytics and Microsoft threat intelligence to detect previously undetected threats. Provides built-in rules and supports mapping security incidents to the MITRE ATT&CK framework for visibility when applicable or available.
• Interactive workbooks: Offers templates for visual reporting and analysis, allowing security teams to quickly gain insights from collected data.
• Incident investigation tools: Features interactive graphs to map and analyze relationships between entities and incidents, attempting to aid in root cause analysis and threat scope assessment.
• Proactive threat hunting: Assists analysts with hunting tools based on the MITRE framework to query data and surface insights before alerts are triggered. Integrates Jupyter notebooks for analytics and data visualization.

5. Elastic Security

Elastic Security is a SIEM solution built on the open-source Elasticsearch platform. It enables organizations to detect, investigate, and respond to cyber threats with visibility and analytics. Elastic’s customizable detection rules and scalability are intended to help SOC teams to modernize their workflows.

Key features include:

• Data visibility: Analyzes data across on-premises, cloud, and hybrid environments.
• Threat detection: Detection rules from Elastic Security Labs are aligned with the MITRE ATT&CK framework in hopes of automating threat identification and reducing noise. Custom ML models enable detection of unknown threats without requiring a data science background.
• Generative AI for workflow acceleration: Assists the SOC with generative AI tools to assist with triage, investigation, and response.
• AI-powered triage: Leverages AI to filter vast volumes of alerts, focusing only on critical incidents. Risk scoring intends to prioritize and address high-impact threats.
• Investigation and response: Offers access to investigation guides and contextual insights. Interactive timelines and piped queries aid threat analysis, while remote host inspection and SOAR integrations intend to optimize incident response workflows.

Conclusion

Splunk SIEM is a tool addressing the growing complexity of cyber threats through analytics, machine learning, and automation. By integrating diverse data sources, streamlining workflows, and enhancing threat detection capabilities, such platforms intend to empower security teams to respond more effectively to incidents. However, organizations must carefully evaluate their needs, technical resources, and budget to ensure the chosen solution aligns with their operational goals and provides a sustainable security strategy.

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